internal MatchAllScorer(MatchAllDocsQuery enclosingInstance, IndexReader reader, Similarity similarity, Weight w, byte[] norms):base(similarity)
{
InitBlock(enclosingInstance);
this.termDocs = reader.TermDocs(null);
score = w.GetValue();
this.norms = norms;
}
private float freq; //prhase frequency in current doc as computed by phraseFreq().
internal PhraseScorer(Weight weight, TermPositions[] tps, int[] offsets, Similarity similarity, byte[] norms):base(similarity)
{
this.norms = norms;
this.weight = weight;
this.value_Renamed = weight.GetValue();
// convert tps to a list of phrase positions.
// note: phrase-position differs from term-position in that its position
// reflects the phrase offset: pp.pos = tp.pos - offset.
// this allows to easily identify a matching (exact) phrase
// when all PhrasePositions have exactly the same position.
for (int i = 0; i < tps.Length; i++)
{
PhrasePositions pp = new PhrasePositions(tps[i], offsets[i]);
if (last != null)
{
// add next to end of list
last.next = pp;
}
else
{
first = pp;
}
last = pp;
}
pq = new PhraseQueue(tps.Length); // construct empty pq
first.doc = - 1;
}
public TermWeight(TermQuery enclosingInstance, Searcher searcher)
{
InitBlock(enclosingInstance);
this.similarity = Enclosing_Instance.GetSimilarity(searcher);
idfExp = similarity.IdfExplain(Enclosing_Instance.term, searcher);
idf = idfExp.GetIdf();
}
public SpanWeight(SpanQuery query, Searcher searcher)
{
this.similarity = query.GetSimilarity(searcher);
this.query = query;
terms = new System.Collections.Hashtable();
query.ExtractTerms(terms);
idfExp = similarity.idfExplain(new System.Collections.ArrayList(terms.Values), searcher);
idf = idfExp.GetIdf();
}
/// <summary> Construct a <code>TermScorer</code>.
///
/// </summary>
/// <param name="weight">The weight of the <code>Term</code> in the query.
/// </param>
/// <param name="td">An iterator over the documents matching the <code>Term</code>.
/// </param>
/// <param name="similarity">The <code>Similarity</code> implementation to be used for score
/// computations.
/// </param>
/// <param name="norms">The field norms of the document fields for the <code>Term</code>.
/// </param>
public /*internal*/ TermScorer(Weight weight, TermDocs td, Similarity similarity, byte[] norms):base(similarity)
{
this.weight = weight;
this.termDocs = td;
this.norms = norms;
this.weightValue = weight.GetValue();
for (int i = 0; i < SCORE_CACHE_SIZE; i++)
scoreCache[i] = GetSimilarity().Tf(i) * weightValue;
}
public ConjunctionScorer(Similarity similarity, Scorer[] scorers):base(similarity)
{
this.scorers = scorers;
coord = similarity.Coord(scorers.Length, scorers.Length);
for (int i = 0; i < scorers.Length; i++)
{
if (scorers[i].NextDoc() == NO_MORE_DOCS)
{
// If even one of the sub-scorers does not have any documents, this
// scorer should not attempt to do any more work.
lastDoc = NO_MORE_DOCS;
return ;
}
}
// Sort the array the first time...
// We don't need to sort the array in any future calls because we know
// it will already start off sorted (all scorers on same doc).
// note that this comparator is not consistent with equals!
System.Array.Sort(scorers, new AnonymousClassComparator(this));
// NOTE: doNext() must be called before the re-sorting of the array later on.
// The reason is this: assume there are 5 scorers, whose first docs are 1,
// 2, 3, 5, 5 respectively. Sorting (above) leaves the array as is. Calling
// doNext() here advances all the first scorers to 5 (or a larger doc ID
// they all agree on).
// However, if we re-sort before doNext() is called, the order will be 5, 3,
// 2, 1, 5 and then doNext() will stop immediately, since the first scorer's
// docs equals the last one. So the invariant that after calling doNext()
// all scorers are on the same doc ID is broken.
if (DoNext() == NO_MORE_DOCS)
{
// The scorers did not agree on any document.
lastDoc = NO_MORE_DOCS;
return ;
}
// If first-time skip distance is any predictor of
// scorer sparseness, then we should always try to skip first on
// those scorers.
// Keep last scorer in it's last place (it will be the first
// to be skipped on), but reverse all of the others so that
// they will be skipped on in order of original high skip.
int end = scorers.Length - 1;
int max = end >> 1;
for (int i = 0; i < max; i++)
{
Scorer tmp = scorers[i];
int idx = end - i - 1;
scorers[i] = scorers[idx];
scorers[idx] = tmp;
}
}
/// <summary> Creates a new instance of DisjunctionMaxScorer
///
/// </summary>
/// <param name="tieBreakerMultiplier">Multiplier applied to non-maximum-scoring subqueries for a
/// document as they are summed into the result.
/// </param>
/// <param name="similarity">-- not used since our definition involves neither coord nor terms
/// directly
/// </param>
/// <param name="subScorers">The sub scorers this Scorer should iterate on
/// </param>
/// <param name="numScorers">The actual number of scorers to iterate on. Note that the array's
/// length may be larger than the actual number of scorers.
/// </param>
public DisjunctionMaxScorer(float tieBreakerMultiplier, Similarity similarity, Scorer[] subScorers, int numScorers):base(similarity)
{
this.tieBreakerMultiplier = tieBreakerMultiplier;
// The passed subScorers array includes only scorers which have documents
// (DisjunctionMaxQuery takes care of that), and their nextDoc() was already
// called.
this.subScorers = subScorers;
this.numScorers = numScorers;
Heapify();
}
private void InitBlock(int df, int max, float idf, Similarity enclosingInstance)
{
this.df = df;
this.max = max;
this.idf = idf;
this.enclosingInstance = enclosingInstance;
}
public ConstantWeight(ConstantScoreQuery enclosingInstance, Searcher searcher)
{
InitBlock(enclosingInstance);
this.similarity = Enclosing_Instance.GetSimilarity(searcher);
}
public ConstantScorer(ConstantScoreQuery enclosingInstance, Similarity similarity, IndexReader reader, Weight w):base(similarity)
{
InitBlock(enclosingInstance);
theScore = w.GetValue();
DocIdSet docIdSet = Enclosing_Instance.filter.GetDocIdSet(reader);
if (docIdSet == null)
{
docIdSetIterator = DocIdSet.EMPTY_DOCIDSET.Iterator();
}
else
{
DocIdSetIterator iter = docIdSet.Iterator();
if (iter == null)
{
docIdSetIterator = DocIdSet.EMPTY_DOCIDSET.Iterator();
}
else
{
docIdSetIterator = iter;
}
}
}
public BooleanWeight(BooleanQuery enclosingInstance, Searcher searcher)
{
InitBlock(enclosingInstance);
this.similarity = Enclosing_Instance.GetSimilarity(searcher);
weights = new System.Collections.ArrayList(Enclosing_Instance.clauses.Count);
for (int i = 0; i < Enclosing_Instance.clauses.Count; i++)
{
BooleanClause c = (BooleanClause) Enclosing_Instance.clauses[i];
weights.Add(c.GetQuery().CreateWeight(searcher));
}
}
public AnonymousClassIDFExplanation2(float idf, Similarity enclosingInstance)
{
InitBlock(idf, enclosingInstance);
}
public AnonymousClassIDFExplanation1(int df, int max, float idf, Similarity enclosingInstance)
{
InitBlock(df, max, idf, enclosingInstance);
}
/// <summary>Set the default Similarity implementation used by indexing and search
/// code.
///
/// </summary>
/// <seealso cref="Searcher.SetSimilarity(Similarity)">
/// </seealso>
/// <seealso cref="Mono.Lucene.Net.Index.IndexWriter.SetSimilarity(Similarity)">
/// </seealso>
public static void SetDefault(Similarity similarity)
{
Similarity.defaultImpl = similarity;
}
internal SloppyPhraseScorer(Weight weight, TermPositions[] tps, int[] offsets, Similarity similarity, int slop, byte[] norms):base(weight, tps, offsets, similarity, norms)
{
this.slop = slop;
}
private void InitBlock(float idf, Similarity enclosingInstance)
{
this.idf = idf;
this.enclosingInstance = enclosingInstance;
}
public AnonymousClassIDFExplanation3(float fIdf, System.Text.StringBuilder exp, Similarity enclosingInstance)
{
InitBlock(fIdf, exp, enclosingInstance);
}
/// <summary>Expert: Set the Similarity implementation used by this Searcher.
///
/// </summary>
/// <seealso cref="Similarity.SetDefault(Similarity)">
/// </seealso>
public virtual void SetSimilarity(Similarity similarity)
{
this.similarity = similarity;
}
public override Explanation Explain(IndexReader reader, int doc)
{
ComplexExplanation result = new ComplexExplanation();
result.SetDescription("weight(" + GetQuery() + " in " + doc + "), product of:");
Explanation idfExpl = new Explanation(idf, "idf(" + GetQuery() + ")");
// explain query weight
Explanation queryExpl = new Explanation();
queryExpl.SetDescription("queryWeight(" + GetQuery() + "), product of:");
Explanation boostExpl = new Explanation(Enclosing_Instance.GetBoost(), "boost");
if (Enclosing_Instance.GetBoost() != 1.0f)
{
queryExpl.AddDetail(boostExpl);
}
queryExpl.AddDetail(idfExpl);
Explanation queryNormExpl = new Explanation(queryNorm, "queryNorm");
queryExpl.AddDetail(queryNormExpl);
queryExpl.SetValue(boostExpl.GetValue() * idfExpl.GetValue() * queryNormExpl.GetValue());
result.AddDetail(queryExpl);
// explain field weight
ComplexExplanation fieldExpl = new ComplexExplanation();
fieldExpl.SetDescription("fieldWeight(" + GetQuery() + " in " + doc + "), product of:");
Scorer scorer = Scorer(reader, true, false);
if (scorer == null)
{
return(new Explanation(0.0f, "no matching docs"));
}
Explanation tfExpl = scorer.Explain(doc);
fieldExpl.AddDetail(tfExpl);
fieldExpl.AddDetail(idfExpl);
Explanation fieldNormExpl = new Explanation();
byte[] fieldNorms = reader.Norms(Enclosing_Instance.field);
float fieldNorm = fieldNorms != null?Similarity.DecodeNorm(fieldNorms[doc]) : 1.0f;
fieldNormExpl.SetValue(fieldNorm);
fieldNormExpl.SetDescription("fieldNorm(field=" + Enclosing_Instance.field + ", doc=" + doc + ")");
fieldExpl.AddDetail(fieldNormExpl);
fieldExpl.SetMatch(tfExpl.IsMatch());
fieldExpl.SetValue(tfExpl.GetValue() * idfExpl.GetValue() * fieldNormExpl.GetValue());
result.AddDetail(fieldExpl);
System.Boolean?tempAux = fieldExpl.GetMatch();
result.SetMatch(tempAux);
// combine them
result.SetValue(queryExpl.GetValue() * fieldExpl.GetValue());
if (queryExpl.GetValue() == 1.0f)
{
return(fieldExpl);
}
return(result);
}
internal ExactPhraseScorer(Weight weight, TermPositions[] tps, int[] offsets, Similarity similarity, byte[] norms):base(weight, tps, offsets, similarity, norms)
{
}
public ConjunctionScorer(Similarity similarity, System.Collections.ICollection scorers):this(similarity, (Scorer[]) new System.Collections.ArrayList(scorers).ToArray(typeof(Scorer)))
{
}
public /*internal*/ BooleanScorer(Similarity similarity, int minNrShouldMatch, System.Collections.IList optionalScorers, System.Collections.IList prohibitedScorers):base(similarity)
{
InitBlock();
this.minNrShouldMatch = minNrShouldMatch;
if (optionalScorers != null && optionalScorers.Count > 0)
{
for (System.Collections.IEnumerator si = optionalScorers.GetEnumerator(); si.MoveNext(); )
{
Scorer scorer = (Scorer) si.Current;
maxCoord++;
if (scorer.NextDoc() != NO_MORE_DOCS)
{
scorers = new SubScorer(scorer, false, false, bucketTable.NewCollector(0), scorers);
}
}
}
if (prohibitedScorers != null && prohibitedScorers.Count > 0)
{
for (System.Collections.IEnumerator si = prohibitedScorers.GetEnumerator(); si.MoveNext(); )
{
Scorer scorer = (Scorer) si.Current;
int mask = nextMask;
nextMask = nextMask << 1;
prohibitedMask |= mask; // update prohibited mask
if (scorer.NextDoc() != NO_MORE_DOCS)
{
scorers = new SubScorer(scorer, false, true, bucketTable.NewCollector(mask), scorers);
}
}
}
coordFactors = new float[maxCoord];
Similarity sim = GetSimilarity();
for (int i = 0; i < maxCoord; i++)
{
coordFactors[i] = sim.Coord(i, maxCoord - 1);
}
}
public MultiPhraseWeight(MultiPhraseQuery enclosingInstance, Searcher searcher)
{
InitBlock(enclosingInstance);
this.similarity = Enclosing_Instance.GetSimilarity(searcher);
// compute idf
System.Collections.IEnumerator i = Enclosing_Instance.termArrays.GetEnumerator();
while (i.MoveNext())
{
Term[] terms = (Term[]) i.Current;
for (int j = 0; j < terms.Length; j++)
{
idf += Enclosing_Instance.GetSimilarity(searcher).Idf(terms[j], searcher);
}
}
}
private void InitBlock(float fIdf, System.Text.StringBuilder exp, Similarity enclosingInstance)
{
this.fIdf = fIdf;
this.exp = exp;
this.enclosingInstance = enclosingInstance;
}
// constructor
internal ValueSourceScorer(ValueSourceQuery enclosingInstance, Similarity similarity, IndexReader reader, ValueSourceWeight w):base(similarity)
{
InitBlock(enclosingInstance);
this.weight = w;
this.qWeight = w.GetValue();
// this is when/where the values are first created.
vals = Enclosing_Instance.valSrc.GetValues(reader);
termDocs = reader.TermDocs(null);
}
public ValueSourceWeight(ValueSourceQuery enclosingInstance, Searcher searcher)
{
InitBlock(enclosingInstance);
this.similarity = Enclosing_Instance.GetSimilarity(searcher);
}
private int maxDoc; // document count
public CachedDfSource(System.Collections.IDictionary dfMap, int maxDoc, Similarity similarity)
{
this.dfMap = dfMap;
this.maxDoc = maxDoc;
SetSimilarity(similarity);
}
/// <summary>Construct a {@link Similarity} that delegates all methods to another.
///
/// </summary>
/// <param name="delegee">the Similarity implementation to delegate to
/// </param>
public SimilarityDelegator(Similarity delegee)
{
this.delegee = delegee;
}
protected internal System.Collections.ArrayList weights = new System.Collections.ArrayList(); // The Weight's for our subqueries, in 1-1 correspondence with disjuncts
/* Construct the Weight for this Query searched by searcher. Recursively construct subquery weights. */
public DisjunctionMaxWeight(DisjunctionMaxQuery enclosingInstance, Searcher searcher)
{
InitBlock(enclosingInstance);
this.similarity = searcher.GetSimilarity();
for (System.Collections.IEnumerator iter = Enclosing_Instance.disjuncts.GetEnumerator(); iter.MoveNext(); )
{
weights.Add(((Query) iter.Current).CreateWeight(searcher));
}
}
public MatchAllDocsWeight(MatchAllDocsQuery enclosingInstance, Searcher searcher)
{
InitBlock(enclosingInstance);
this.similarity = searcher.GetSimilarity();
}
/// <summary>Constructs a Scorer.</summary>
/// <param name="similarity">The <code>Similarity</code> implementation used by this scorer.
/// </param>
protected internal Scorer(Similarity similarity)
{
this.similarity = similarity;
}
static TermScorer()
{
SIM_NORM_DECODER = Similarity.GetNormDecoder();
}
